In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …

Stochastic search algorithms are general black-box optimizers. Due to their ease of use and
their generality, they have recently also gained a lot of attention in operations research …

This article deals with Gaussian process surrogate models for the Covariance Matrix
Adaptation Evolutionary Strategy (CMA-ES)—several already existing and two by the …

Automated algorithm selection and configuration methods that build on exploratory
landscape analysis (ELA) are becoming very popular in Evolutionary Computation …

In this paper, three extensions of the BI-population Covariance Matrix Adaptation Evolution
Strategy with weighted active covariance matrix update (BIPOP-aCMA-ES) are investigated …

In the last decades, a number of novel meta-heuristics and hybrid algorithms have been
proposed to solve a great variety of optimization problems. Among these, constrained …

Surrogate regression models have been shown as a valuable technique in evolutionary
optimization to save evaluations of expensive black-box objective functions. Each surrogate …

This paper presents a new variant of surrogate-model utilization in expensive continuous
evolutionary black-box optimization. This algorithm is based on the surrogate version of the …

Evaluation of real-world black-box objective functions is in many optimization problems very
time-consuming or expensive. Therefore, surrogate regression models, used instead of the …

The global optimization of expensive-to-calculate continuous functions is of great practical
importance in engineering. Among the proposed algorithms for solving such problems …